Eddie Aguilar Ernesto Morales
library ("easypackages" )packages ("tidyverse" ,"fpp3" , "tsibble" , "feasts" ,"fable" , "patchwork" )
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# A tsibble: 52,608 x 5 [30m] <Australia/Melbourne>
Time Demand Temperature Date Holiday
<dttm> <dbl> <dbl> <date> <lgl>
1 2012-01-01 00:00:00 4383. 21.4 2012-01-01 TRUE
2 2012-01-01 00:30:00 4263. 21.0 2012-01-01 TRUE
3 2012-01-01 01:00:00 4049. 20.7 2012-01-01 TRUE
4 2012-01-01 01:30:00 3878. 20.6 2012-01-01 TRUE
5 2012-01-01 02:00:00 4036. 20.4 2012-01-01 TRUE
6 2012-01-01 02:30:00 3866. 20.2 2012-01-01 TRUE
7 2012-01-01 03:00:00 3694. 20.1 2012-01-01 TRUE
8 2012-01-01 03:30:00 3562. 19.6 2012-01-01 TRUE
9 2012-01-01 04:00:00 3433. 19.1 2012-01-01 TRUE
10 2012-01-01 04:30:00 3359. 19.0 2012-01-01 TRUE
# … with 52,598 more rows
Usando datos cada hora:
<- index_by (vic_elec, time = ~ floor_date (., unit= "hour" )) |> summarise (demand = sum (Demand),mean_temp = mean (Temperature),max_temp = max (Temperature),holiday = any (Holiday)) |> mutate (day_type = case_when (~ "holiday" ,wday (time) %in% 2 : 6 ~ "weekday" ,TRUE ~ "weekend"
# A tsibble: 26,304 x 6 [1h] <Australia/Melbourne>
time demand mean_temp max_temp holiday day_type
<dttm> <dbl> <dbl> <dbl> <lgl> <chr>
1 2012-01-01 00:00:00 8646. 21.2 21.4 TRUE holiday
2 2012-01-01 01:00:00 7927. 20.6 20.7 TRUE holiday
3 2012-01-01 02:00:00 7902. 20.3 20.4 TRUE holiday
4 2012-01-01 03:00:00 7256. 19.8 20.1 TRUE holiday
5 2012-01-01 04:00:00 6793. 19.0 19.1 TRUE holiday
6 2012-01-01 05:00:00 6636. 18.7 18.8 TRUE holiday
7 2012-01-01 06:00:00 6548. 18.7 18.8 TRUE holiday
8 2012-01-01 07:00:00 6865. 19.6 20.1 TRUE holiday
9 2012-01-01 08:00:00 7300. 21.8 22.6 TRUE holiday
10 2012-01-01 09:00:00 8002. 24.6 25.2 TRUE holiday
# … with 26,294 more rows
<- autoplot (vic_elec_hour, demand, color = "blue" )ggplotly (p, dynamicTicks = TRUE ) |> rangeslider ()
|> ggplot (aes (x = mean_temp, y = demand, color = holiday)) + geom_point (alpha = 0.5 )
|> ggplot (aes (x = mean_temp, y = demand, color = day_type)) + geom_point (alpha = 0.5 )
<- filter (vic_elec_hour, year (time) %in% 2012 : 2015 ))
# A tsibble: 26,304 x 6 [1h] <Australia/Melbourne>
time demand mean_temp max_temp holiday day_type
<dttm> <dbl> <dbl> <dbl> <lgl> <chr>
1 2012-01-01 00:00:00 8646. 21.2 21.4 TRUE holiday
2 2012-01-01 01:00:00 7927. 20.6 20.7 TRUE holiday
3 2012-01-01 02:00:00 7902. 20.3 20.4 TRUE holiday
4 2012-01-01 03:00:00 7256. 19.8 20.1 TRUE holiday
5 2012-01-01 04:00:00 6793. 19.0 19.1 TRUE holiday
6 2012-01-01 05:00:00 6636. 18.7 18.8 TRUE holiday
7 2012-01-01 06:00:00 6548. 18.7 18.8 TRUE holiday
8 2012-01-01 07:00:00 6865. 19.6 20.1 TRUE holiday
9 2012-01-01 08:00:00 7300. 21.8 22.6 TRUE holiday
10 2012-01-01 09:00:00 8002. 24.6 25.2 TRUE holiday
# … with 26,294 more rows
# maximos: # year = 4380 # week = 84 # day = 12 <- train |> model (Fourier = TSLM (demand ~ mean_temp + I (mean_temp^ 2 ) + day_type + fourier (period = "year" , K = 10 ) + fourier (period = "week" , K = 8 ) + fourier (period = "day" , K = 6 )))glance (fit)
# A tibble: 1 × 15
.model r_squared adj_r_s…¹ sigma2 stati…² p_value df log_lik AIC AICc
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 Fourier 0.859 0.859 4.30e5 3193. 0 51 -2.08e5 3.41e5 3.41e5
# … with 5 more variables: BIC <dbl>, CV <dbl>, deviance <dbl>,
# df.residual <int>, rank <int>, and abbreviated variable names
# ¹adj_r_squared, ²statistic
# A tsibble: 26,304 x 6 [1h] <Australia/Melbourne>
# Key: .model [1]
.model time demand .fitted .resid .innov
<chr> <dttm> <dbl> <dbl> <dbl> <dbl>
1 Fourier 2012-01-01 00:00:00 8646. 5763. 2883. 2883.
2 Fourier 2012-01-01 01:00:00 7927. 5305. 2622. 2622.
3 Fourier 2012-01-01 02:00:00 7902. 4600. 3302. 3302.
4 Fourier 2012-01-01 03:00:00 7256. 3875. 3381. 3381.
5 Fourier 2012-01-01 04:00:00 6793. 3387. 3406. 3406.
6 Fourier 2012-01-01 05:00:00 6636. 3355. 3281. 3281.
7 Fourier 2012-01-01 06:00:00 6548. 3840. 2708. 2708.
8 Fourier 2012-01-01 07:00:00 6865. 4715. 2150. 2150.
9 Fourier 2012-01-01 08:00:00 7300. 5622. 1679. 1679.
10 Fourier 2012-01-01 09:00:00 8002. 6341. 1661. 1661.
# … with 26,294 more rows
ggplot () + geom_line (vic_elec_hour, mapping = aes (x = time, y = demand)) + geom_line (aug, mapping = aes (x = time, y = .fitted), color = "blue" , alpha = 0.8 )
